Identification and control of myopic progression using distortion

ABSTRACT

Provided are systems (e.g., a modified fundus camera system) and methods for measuring ocular distortion, the methods comprising projecting an image of a known target pattern having characteristic features onto an area of a retinal plane/surface to provide a distorted retinal image of the target pattern across the area of the retinal surface, recording the distorted retinal image of the target pattern using an image sensor to provide a captured distorted retinal image of the target pattern across the area of the retinal surface, identifying the characteristic features of the captured distorted retinal image, and comparing the identified characteristic features of the captured distorted retinal image of the target pattern across the area of the retinal surface to corresponding characteristic features of the known target pattern to provide a map of ocular distortion across the area of the retinal surface. Also provided are systems and methods for measuring retinal shape.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates generally to myopia and myopicprogression, and more particularly to imaging systems and non-invasivemethods for identification and characterization (e.g. quantifiablemeasurement of the amount, type and orientation of ocular distortionpresent in a human eye) of ocular distortion to detect the onset orprogression of myopia and facilitate control of myopic progression.Additional aspects relate to methods for measuring [characterizing]retinal shape (e.g., a radius and shape, e.g., conic shape).

Description of the Related Art

Visual acuity is the measure of the human eye to perfectly resolveimages from the real world onto the retina of the eye. To resolve theseimages in space, the main determinants of refraction (e.g., ability ofthe eye to bend light so that an image is focused on the retina) are thefocusing power of the cornea, the crystalline lens and the length of theeye. A reduction in visual acuity from images being focused in front ofthe retinal plane, due to a higher curvature in the cornea or the lengthof the eye being too long is defined as myopia (axial myopia). Whenimages are perfectly formed on the retina, this state is calledemmetropia and an image focused behind the retinal plane is defined ashyperopia. The focusing power of the eye is often defined in units ofdiopters (D).

Several investigations into the optics of the human eye regarding themeasurement of ocular surfaces or ocular aberrations have been conducted(Liang J, et al., “Objective measurement of wave aberrations of thehuman eye with the use of a Hartmann-Shack wave-front sensor,” J Opt SocAm A., 11:1949-1957 (1994); Cheng, X., et al., “Relationship betweenrefractive error and monochromatic aberrations of the eye. Optometry andVision” Science, 80, 43-49 (2003); Lourdes Llorente, et al., “Myopicversus hyperopic eyes: axial length, corneal shape and opticalaberrations,” Journal of Vision, 4(4):5. doi: 10.1167/4.4.5 (2004);Susana Marcos, et al., “Investigating sources of variability ofmonochromatic and transverse chromatic aberrations across eyes,” VisionResearch, Volume 41, Issue 28, 3861-3871 (2001); Mathur, A., et al.,“Myopia and peripheral ocular aberrations,” Journal of Vision, 9(10):15,1-12 (2009); Hartwig A, & Atchison DA “Analysis of higher-orderaberrations in a large clinical population,” Invest Ophthalmol Vis Sci.,53:7862-7870 (2012), http://dx.doi.org/10.1167/iovs.12-10610; andBuehren, Tobias et al, “The Stability of Corneal Topography in thePost-Blink Interval,” Cornea. 20. 826-33 (2001),10.1097/00003226-200111000-00010), and have significant relevance toocular health, quality of life, and scientific advancement. From theoptical engineer's point of view, ocular aberration of the human eye isheavily influenced by the imaging optics, cornea, and crystalline lens.The predominantly studied and often corrected ocular aberrations arepower error or defocus, astigmatism, coma and spherical aberration. Bycontrast, there has been relatively little work done in the field ofoptical engineering to investigate the effects of distortion, ageometrical aberration, on the human visual experience, or toinvestigate the related biological mechanisms. Distortion is typicallyignored because traditional wavefront sensors measure aberrations at asingle field point for each measurement. To measure distortion, manyfield points would need to be measured simultaneously.

In ophthalmology, various retinal pathologies such as maculardegeneration or diabetic retinopathy require intervention or treatmentafter discovery. Optical coherence tomography, MRI fluorescence imagingare some techniques used to investigate the surface of the retina.Fundus photography allows for real color imaging of the retinal surfacewith low invasive procedural steps, and at a relatively low cost. TheAmsler Grid Test provides a subjective test for patients to reportdeviations across a grid to potentially indicate the onset of an ailmentsuch as macular degeneration and corneal edema.

Myopia. Myopia affects nearly one in three people in the United Statesand reportedly up to 80% of people in East Asian countries. When myopiamoves past moderate levels (>6.00D spherical equivalent power), seriousocular ailments can occur ranging from retinal detachment to cataractsand glaucoma. Beyond these extremes, ocular corrections related tomyopia create healthcare costs in the billions and reduce the quality oflife for those suffering from this condition.

Methods aimed at correcting myopia and controlling its progression arewell documented. Single vision spectacle lenses, aspheric spectaclelenses, bifocal spectacle lenses, soft contact lenses, multifocalcontact lenses, rigid gas permeable lenses, orthokeratology (OK) are allexamples of myopic treatment and control. Some methods such as spectaclewear, try to bring distant images to the focal plane of the retina bycounteracting the optical power of the cornea or overcoming an axiallyelongated vitreous chamber. Other methods such as OK or rigid contactlens wear try to reshape the cornea to provide the wearer with clear dayvision or try to correct peripheral refraction and delay the progressionof myopia. However, the long-term effectiveness of these treatmentsremains an open area of research and the greatest benefits of thesetreatments are seen at an early intervention point. However, signalsthat can be used to determine the onset and progression rate of myopiahave not been identified.

While the exact mechanisms and their impact for onset and progression ofmyopia remain an active area of research, several studies point tofactors such as ethnicity, age, genetics and environmental visualstimuli as some of the most predominant factors. The prevailing theorybehind the progression of myopia is that the eye has a lot ofastigmatism in peripheral vision. This astigmatism in turn can provide asignal to trigger eye growth. In myopia, this trigger is somehow faultyand a feedback loop is created to continuously increase eye growthleading to high levels of myopia. Various correction modalities such ascontact lenses or spectacles are attempts to interfere with thisastigmatism signal and disrupt the feedback loop. The mechanism is notunderstood and these modalities have demonstrated some effect in somepatients, but fail in other patients where the progression of myopiacontinues and becomes more severe for the individual.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 shows a wide range of several possible types of distortion.Distortion creates non-uniformity and a bending of straight-line objectsor rectilinear objects.

FIG. 2 shows a schematic of a traditional fundus camera system fromZeiss (reproduced from Wiggins et al., Applied Optics 1:179-181 (1972).

FIG. 3 shows, according to particular exemplary aspects of the presentinvention, a wire grating projected onto a human retina and imaged usinga modified fundus camera as disclosed and described herein.

FIG. 4 shows, according to particular exemplary aspects of the presentinvention, distortion pattern detection using a point extractionsoftware package.

FIG. 5 shows, according to particular exemplary aspects of the presentinvention, an ocular distortion map. The undistorted grid is shown ingray with the ocular distortion pattern shown in black.

FIG. 6 shows, according to particular exemplary aspects of the presentinvention, comparison of distortion between small and large retinalcurvatures

FIG. 7 shows, according to particular exemplary aspects of the presentinvention, a source image and annulus mask at location 3 that areconjugate to the holed mirror at location 8. The dot grid pattern atlocation 5 is picked up along the illumination path and is conjugate tothe retina by the holed mirror.

FIG. 8 shows, according to particular exemplary aspects of the presentinvention, a 3D printed cover and grid target holder. The grid target,fixed to a glass plate, is placed conjugate to the intermediate retina.

FIG. 9 shows, according to particular exemplary aspects of the presentinvention, the retinal illumination and return path of the modifiedfundus camera. The holed mirror image at location 8 is conjugate to thepupil of the eye located at 11. An intermediate image of the retina withthe grid target projection becomes the object presented to the imagingpathway for detection.

FIG. 10 shows, according to particular exemplary aspects of the presentinvention, four retinal images with the projected grid pattern fromcohort subjects. Self-reported refractive error is listed above eachimage.

FIG. 11 shows, according to particular exemplary aspects of the presentinvention, four retinal images with the projected grid pattern fromdifferent cohort subjects. Self-reported refractive error is listedabove each image.

FIG. 12 shows, according to particular exemplary aspects of the presentinvention, four retinal images with the projected grid pattern from yetdifferent cohort subjects. Self-reported refractive error is listedabove each image.

FIG. 13 shows, according to particular exemplary aspects of the presentinvention, four retinal images with the projected grid pattern fromadditional different cohort subjects. Self-reported refractive error islisted above each image.

FIG. 14 shows, according to particular exemplary aspects of the presentinvention, the retinal image with the projected grid pattern of a finalcohort subject. Self-reported refractive error is listed above theimage.

FIG. 15 shows, according to particular exemplary aspects of the presentinvention, the final imaging path for the modified fundus camera. Anintermediate image of the retinal surface and grid target is locatedbefore holed mirror at location 8. The location of this intermediateimage is different for subjects with any refractive error. A zoom lenssystem corrects for any added power induced by the eye under test.

FIG. 16 shows, according to particular exemplary aspects of the presentinvention, three processing candidate images that meet the criterion forpost-processing. It is of important note that the pattern does notdeviate significantly even with small eye rotations, supporting that astable, repeatable target can be imaged on the retina.

FIG. 17 shows, according to particular exemplary aspects of the presentinvention, that lateral misalignment with respect to the eye stop causeskeystone distortion. Example images for the calibration system (top) andin a human subject (bottom). In the human subject, the corner oppositethe vignetting side appears to pinch and blur.

FIG. 18 shows, according to particular exemplary aspects of the presentinvention, a filtered Fourier Transform (FT) shown on the left with thebinary resultant image of the retina shown on the right.

FIG. 19 shows, according to particular exemplary aspects of the presentinvention, centroid centers marked in white that were found by theinternal centroid algorithm. Incorrect or misinterpreted centroid valuesare also present in the image and indicate false positive centers.

FIG. 20 shows, according to particular exemplary aspects of the presentinvention, that the method for determining the undistorted referencegrid is done by taking the center dot of the grid pattern at (6,6) andaverage the four nearest neighbor distances seen as black dots. Removedfalse-positive points (white) can be viewed with the automatically foundcenters (light gray) and the user hand-clicked centers (dark gray).

FIG. 21 shows, according to particular exemplary aspects of the presentinvention, Table 1; repeatability results for automatic and manualcenter dot processing. All number are in units of pixel spacecoordinates. Commentary on fit errors related to spacing values ispresented later.

FIG. 22 shows, according to particular exemplary aspects of the presentinvention, linear B coefficient values for human subject 10. Note B₂ andB₃ correspond to x and y linear magnification respectively. Fit valuesare stable and of reasonable magnitude for each processed image.

FIG. 23 shows, according to particular exemplary aspects of the presentinvention, quadratic C coefficient values for subject 10. Fit values for(C₇, C₈, C₉, C₁₃) are stable and of reasonable magnitude for eachprocessed image. Fit values for (C₁₂, C₁₄) are unstable in sign andmagnitude making them inconclusive representations of image distortion.

FIG. 24 shows, according to particular exemplary aspects of the presentinvention, Cubic D coefficient values for subject 10. Fit values for(D₂₃, D₂₆, D₂₇, D₂₈, D₃₀) are stable and of reasonable magnitude foreach processed image. Fit values for (D₂₄, D₂₅, D₂₉) are unstable insign and magnitude making them inconclusive representations of imagedistortion.

FIGS. 25A and 25B show, according to particular exemplary aspects of thepresent invention, mean radial distortion as a percentage plottedagainst the subject refractive error (FIG. 25A) and the simulatedpopulation radial distortion plotted against the subject refractiveerror (FIG. 25B). The square □ and diamond ⋄ markings indicate stronglyresolved and weak or blurred dot patterns respectively. The * markers inblack correspond to subjects who had one or more radial distortioncoefficient values span zero, indicating inconclusive results.

FIG. 26 shows, according to particular exemplary aspects of the presentinvention, mean RMS Fit error for all subjects. All subjects seeimproved fitting out the 4^(th) order wavefront error. Error units arein pixels. Larger error is attributed to weak dot contrast, low numbersof points found, and higher order distortion.

FIG. 27 shows a summary of three basic functions relating to measurementof ocular distortion according to particular exemplary aspects of thepresent invention. First, a known target pattern is projected onto theretinal surface. Second, an image sensor captures the image of thetarget on the retinal surface. Third, post processing of targetfiducials back out useful information related to ocular distortion.

FIG. 28 shows an exemplary block diagram of hardware and an operatingenvironment in conjunction with which implementations of the disclosedmethods for measuring ocular distortion may be practiced.

FIG. 29 shows an exemplary block diagram illustrating a mobilecommunication device 300 that may be used to implement one or more ofthe computing devices of the system.

SUMMARY OF EXEMPLARY ASPECTS OF THE INVENTION

Provided is a new theory for understanding myopic onset and progressionbased on measuring (e.g., quantifying) distortion of an eye, and wherecharacterization of ocular distortion provides for detecting the onsetor progression of myopia. No such systems currently exist to measure theocular distortion.

Particular aspects of the present invention provide systems and methodsfor measuring and quantifying distortion in the human eye as a predictorof myopia.

The systems and methods provide for quantifiable characterization ofdistortion in the human eye using a non-invasive imaging technique. Inthe methods, the amount, type and orientation of ocular distortion maybe quantified, for example, using a modified fundus camera having atarget pattern (e.g. grid or dot pattern) positioned in a plane of theillumination path conjugate to a retinal plane, and optimally usingsoftware extraction method(s) (e.g., image processing, variablefrequency pass methods, mathematical fitting algorithms, tracking,alignment, etc.)

The systems and methods may comprise placing a target pattern in theillumination path of a fundus camera at a location that is conjugate toa retina, wherein the target pattern (e.g., grid pattern, concentriccircles, arrays of dots, etc.) may comprise a known pattern thatcontains useful fiducials for quantifying ocular distortion.

The systems and methods may comprise placing a phase target in theillumination path of a fundus camera at a location that is a Fouriertransform plane of the retina, wherein the phase target may have asuitable pattern (e.g., grid pattern, concentric circles, arrays ofdots, etc.) such that its Fourier transform creates a suitable amplitudepattern on the retina for measuring ocular distortion.

The systems and methods may comprise an image sensor in the imaging paththat records images of the target projected onto the retina.

The systems and methods may comprise real-time image processing softwarethat examines features in the retinal image (e.g., blood vessels) toprovide autofocus of the image and to ensure alignment of the eyebetween consecutive images.

The systems and methods may comprise hardware and/or software configuredto track the eye (e.g., human eye) and gaze direction, ensuring eyealignment and providing useful information on eye orientation duringdistortion measurement.

The systems and methods may comprise software configured to analyze thecaptured image and automatically identify features in the targetpattern. The features may then be compared to the known target patternto quantify the amount of ocular distortion.

Additional systems and methods provide for measuring retinal shape, andmay comprise software configured to enable subjects to subjectivelycorrect a pre-distorted target and, using tracking methods, back outresidual distortion and characteristics of retinal shape or asphericityfrom the corrected target.

Provided are methods for measuring ocular distortion present in an eye,comprising: illuminating a known target pattern having characteristicfeatures and positioned in plane of an illumination path of a retinalimaging system that is conjugate to a retinal plane; projecting an imageof the target pattern onto an area of the retinal plane to provide adistorted retinal image of the target pattern across the area of theretinal surface; projecting/focusing the distorted retinal image of thetarget pattern to an image sensor/detector positioned in an imaging pathof the retinal imaging system; recording the distorted retinal image ofthe target pattern using the image sensor to provide a captureddistorted retinal image of the target pattern across the area of theretinal surface; identifying the characteristic features of the captureddistorted retinal image of the target pattern across the area of theretinal surface; and comparing the identified characteristic features ofthe captured distorted retinal image of the target pattern across thearea of the retinal surface to corresponding characteristic features ofthe known target pattern to provide a map of ocular distortion acrossthe area of the retinal surface.

The methods may comprise quantifying the amount of ocular distortionand/or determining the type of ocular distortion. In the methods, theretinal imaging system may comprise a fundus camera configured toinclude the known target pattern having the characteristic features andpositioned in plane of the illumination path of the fundus camera thatis conjugate to a retinal plane. In the methods, the target pattern maycomprise fiducials suitable for quantifying ocular distortion, thepattern being one or more selected from the amplitude pattern groupconsisting of a rectilinear grid pattern, concentric ring pattern, andvariable density grid pattern, or from the phase pattern groupconsisting of a phase plate, diffractive optic, and null correctingplate. In the methods, the target pattern may be a phase target patternplaced in the illumination path of the retinal imaging system at alocation that is a Fourier transform plane of the retina, the phasetarget having a suitable pattern such that its Fourier transform createsa suitable amplitude pattern on the retina for measuring oculardistortion. In the methods, the amplitude pattern may comprise one ormore of a grid pattern, concentric circles, or arrays of dots. In themethods, identifying the characteristic features of the captureddistorted retinal image of the target pattern across the area of theretinal surface may comprise use of frequency space image processing toautomatically detect centroid signatures from the target pattern. In themethods, a variable frequency pass method may be swept across the imageto remove intensity variations, and suppress strong, e.g., background,objects (e.g., optic disc, blood vessels) while boosting the distortedtarget pattern to the foreground. In the methods, centroid or cornerdetection may be completed to identify various grid positions across theretinal surface. The methods may comprise quantifying the amount ofocular distortion and/or determining the type of ocular distortion usingmathematical fitting. The methods may comprise correlating, using amathematical fitting algorithm, the distorted retinal image of thetarget pattern across the area of the retinal surface, with the knowngrid pattern, free from distortion, across the area of the retinalsurface. The methods may comprise calibration of the retinal imagingsystem for inherent distortion to provide for an absolute measurement ofocular distortion. In the methods, the retinal imaging system may beconfigured to distinguish between distortion caused by retinal imagingsystem misalignment and local changes in retinal topography, to providefor extracting a diagnostic of global and local retinal curvature. Themethods may comprise or further comprise repetition of the method stepsacross one or more additional areas of the retinal surface to provide amap of ocular distortion across a larger retinal area/field, preferablycomprising use of a stitching algorithm. The methods may comprisecombining eye rotation and continuous distortion mapping. The methodsmay comprise or further comprise use of suitable real-time imageprocessing software to examine features in the retinal image (e.g.,blood vessels) to provide autofocus of the image and to ensure alignmentof the eye between consecutive images. In the methods, the suitableretinal imaging system and software may be configured to track the humaneye and gaze direction ensuring eye alignment, to provide usefulinformation on eye orientation during distortion measurement. Themethods may be applied to a subject over time (e.g., applied duringcritical years of eye growth) to provide a method for monitoring ortracking temporal changes or progression in distortion. The methods maycomprise monitoring or tracking the change or progression in distortionrelative to a distortion metric, to provide a method for detecting earlyonset myopia or hyperopia in patients. The methods may comprise orfurther comprise implementing intervention methods (e.g., corrective orcontrol measures for ocular distortion such as spectacle lenses, contactlenses, multifocal lenses and other modalities).

Additionally provided are systems for measuring ocular distortionpresent in an eye, comprising: a retinal imaging system comprising anillumination system having an illumination source and an illuminationpath, and an imaging system having an image sensor/detector and animaging path, wherein the illumination path is configured toproject/focus light from the illumination source to a retinal plane ofan eye to illuminate a retinal surface area, and wherein the imagingpath is configured for projecting scattered light emerging from theilluminated retinal surface area to the image sensor/detector; and aknown target pattern having characteristic features positioned andconfigured in a plane of the illumination path that is conjugate to theretinal plane, such that upon illumination an image of the known targetpattern is projectable onto the retinal surface area to provide for adistorted retinal image of the target pattern across the area of theretinal surface, and wherein the image sensor is configured forrecording of the distorted retinal image of the target pattern acrossthe area of the retinal surface. The systems may comprise or furthercomprise computer implemented software for identifying thecharacteristic features of the captured distorted retinal image of thetarget pattern across the area of the retinal surface, and comparing theidentified characteristic features of the captured distorted retinalimage of the target pattern across the area of the retinal surface tocorresponding characteristic features of the known target pattern toprovide a map of ocular distortion across the area of the retinalsurface. The systems may comprise or further comprise computerimplemented software for real-time image processing of features in theretinal image (e.g., blood vessels) to provide for autofocus of theimage and for eye alignment between consecutive images. In the systems,the retinal imaging system and software may be configured to track theeye and gaze direction to provide for eye alignment, to provide usefulinformation on eye orientation during distortion measurement. In thesystems, the target pattern may comprise fiducials suitable forquantifying ocular distortion, the pattern being at least one selectedfrom the amplitude pattern group consisting of, e.g., a rectilinear gridpattern, concentric ring pattern, and variable density grid pattern, orfrom the phase pattern group consisting of a phase plate, diffractiveoptic, and/or null correcting plate. In the systems, the target patternmay be a phase target pattern placed in the illumination path of theretinal imaging system at a location that is a Fourier transform planeof the retina, the phase target having a suitable pattern such that itsFourier transform creates a suitable amplitude pattern on the retina formeasuring ocular distortion. In the systems, the amplitude pattern maycomprise, e.g., one or more of a grid pattern, concentric circles, orarrays of dots, etc. In the systems, the retinal imaging system maycomprises a fundus camera.

Further provided are methods for measuring retinal shape, comprising:displaying, to a subject, an image of a known distorted target patternhaving characteristic features and characterized in terms of size andshape relative to the distance away from the subject's eye; tracking theedge points of the distorted target pattern during correction orun-distortion of the distorted target pattern by the subject, until thecharacteristic features are undistorted to provide a baseline measurefor the amount of distortion present in the subject's eye; placing alens of a given power in front of the subject's eye; tracking the edgepoints of the distorted target pattern during correction orun-distortion of the distorted target pattern by the subject, until thecharacteristic features are undistorted to provide a measure for theamount of distortion present in the subject's eye; and determining,using the baseline measure and the lens measure, a radius and/or shape(e.g., conic shape) of the subject's retina (based on the presumptionthat the amount of distortion changes equivalently for a given retinalshape). In the methods, the distorted target pattern may comprises agrid pattern having distorted lines, and wherein correction orun-distortion of the distorted target pattern by the subject maycomprise correcting or un-distorting the distorted grid lines of thegrid until they are straight and square. In the methods, correction orun-distortion of the distorted target pattern by the subject andtracking thereof to determine residual distortion and characteristics ofretinal shape or asphericity from the corrected target may comprise useof suitable software (e.g., image processing, mathematical fittingalgorithms, tracking, alignment, etc.).

Embodiments of the disclosure can be described in view of the followingclauses:

1. A method for measuring ocular distortion present in an eye,comprising: illuminating a known target pattern having characteristicfeatures and positioned in plane of an illumination path of a retinalimaging system that is conjugate to a retinal plane;projecting an image of the target pattern onto an area of the retinalplane to provide a distorted retinal image of the target pattern acrossthe area of the retinal surface; projecting the distorted retinal imageof the target pattern to an image sensor positioned in an imaging pathof the retinal imaging system;recording the distorted retinal image of the target pattern using theimage sensor to provide a captured distorted retinal image of the targetpattern across the area of the retinal surface;identifying the characteristic features of the captured distortedretinal image of the target pattern across the area of the retinalsurface; andcomparing the identified characteristic features of the captureddistorted retinal image of the target pattern across the area of theretinal surface to corresponding characteristic features of the knowntarget pattern to provide a map of ocular distortion across the area ofthe retinal surface.2. The method of clause 1, comprising quantifying the amount of oculardistortion and/or determining the type of ocular distortion.3. The method of clause 1 or 2, wherein the retinal imaging systemcomprises a fundus camera configured to include the known target patternhaving the characteristic features and positioned in plane of theillumination path of the fundus camera that is conjugate to a retinalplane.4. The method of any one of clauses 1-3, wherein the target patterncomprises fiducials suitable for quantifying ocular distortion, thepattern being one or more selected from the amplitude pattern groupconsisting of a rectilinear grid pattern, concentric ring pattern, andvariable density grid pattern, or from the phase pattern groupconsisting of a phase plate, diffractive optic, and null correctingplate.5. The method of any one of clauses 1-4, wherein the target pattern is aphase target pattern placed in the illumination path of the retinalimaging system at a location that is a Fourier transform plane of theretina, the phase target having a suitable pattern such that its Fouriertransform creates a suitable amplitude pattern on the retina formeasuring ocular distortion.6. The method of clause 5, wherein the amplitude pattern comprises oneor more of a grid pattern, concentric circles, or arrays of dots.7. The method of any one of clauses 1-6, wherein identifying thecharacteristic features of the captured distorted retinal image of thetarget pattern across the area of the retinal surface, comprises use offrequency space image processing to automatically detect centroidsignatures from the target pattern.8. The method of clause 7, wherein a variable frequency pass method isswept across the image to remove intensity variations, and suppressstrong objects (e.g., optic disc, blood vessels) while boosting thedistorted target pattern to the foreground.9. The method of clause 8, wherein centroid or corner detection iscompleted to identify various grid positions across the retinal surface.10. The method of any one of clauses 1-9, comprising quantifying theamount of ocular distortion and/or determining the type of oculardistortion using mathematical fitting.11. The method of clause 10, comprising correlating, using amathematical fitting algorithm, the distorted retinal image of thetarget pattern across the area of the retinal surface, with the knowngrid pattern, free from distortion, across the area of the retinalsurface.12. The method of clause 11, comprising calibration of the retinalimaging system for inherent distortion to provide for an absolutemeasurement of ocular distortion.13. The method of any one of clauses 1-12, wherein the retinal imagingsystem is configured to distinguish between distortion caused by retinalimaging system misalignment and local changes in retinal topography, toprovide for extracting a diagnostic of global and local retinalcurvature.14. The method of any one of clauses 1-13, further comprising repetitionof the method steps across one or more additional areas of the retinalsurface to provide a map of ocular distortion across a larger retinalarea, preferably comprising use of a stitching algorithm.15. The method of clause 14, comprising combining eye rotation andcontinuous distortion mapping.16. The method of any one of clauses 1-15, further comprising use ofsuitable real-time image processing software to examine features in theretinal image (e.g., blood vessels) to provide autofocus of the imageand to ensure alignment of the eye between consecutive images.17. The method of clause 16, wherein the retinal imaging system andsoftware are configured to track the human eye and gaze directionensuring eye alignment, to provide useful information on eye orientationduring distortion measurement.18. The method of any one of clauses 1-17, applied to a subject overtime, to provide a method for monitoring or tracking temporal changes orprogression in distortion.19. The method of clause 18, applied during critical years of eyegrowth.20. The method of clause 18 or 19, comprising monitoring or tracking thechange or progression in distortion relative to a distortion metric, toprovide a method for detecting early onset myopia or hyperopia inpatients.21. The method of clause 20, further comprising implementingintervention methods (e.g., corrective or control measures for oculardistortion such as spectacle lenses, contact lenses, multifocal lensesand other modalities).22. A system for measuring ocular distortion present in an eye,comprising:a retinal imaging system comprising an illumination system having anillumination source and an illumination path, and an imaging systemhaving an image sensor and an imaging path, wherein the illuminationpath is configured to project light from the illumination source to aretinal plane of an eye to illuminate a retinal surface area, andwherein the imaging path is configured for projecting scattered lightemerging from the illuminated retinal surface area to the image sensor;anda known target pattern having characteristic features positioned andconfigured in a plane of the illumination path that is conjugate to theretinal plane, such that upon illumination an image of the known targetpattern is projectable onto the retinal surface area to provide for adistorted retinal image of the target pattern across the area of theretinal surface, and wherein the image sensor is configured forrecording of the distorted retinal image of the target pattern acrossthe area of the retinal surface.23. The system of clause 22, further comprising computer implementedsoftware for identifying the characteristic features of the captureddistorted retinal image of the target pattern across the area of theretinal surface, and comparing the identified characteristic features ofthe captured distorted retinal image of the target pattern across thearea of the retinal surface to corresponding characteristic features ofthe known target pattern to provide a map of ocular distortion acrossthe area of the retinal surface.24. The system of clauses 22 or 23, further comprising computerimplemented software for real-time image processing of features in theretinal image (e.g., blood vessels) to provide for autofocus of theimage and for eye alignment between consecutive images.25. The system of clause 24, wherein the retinal imaging system andsoftware are configured to track the eye and gaze direction to providefor eye alignment, to provide useful information on eye orientationduring distortion measurement.26. The system of any one of clauses 22-25, wherein the target patterncomprises fiducials suitable for quantifying ocular distortion, thepattern being one or more selected from the amplitude pattern groupconsisting of a rectilinear grid pattern, concentric ring pattern, andvariable density grid pattern, or from the phase pattern groupconsisting of a phase plate, diffractive optic, and null correctingplate.27. The system of any one of clauses 22-26, wherein the target patternis a phase target pattern placed in the illumination path of the retinalimaging system at a location that is a Fourier transform plane of theretina, the phase target having a suitable pattern such that its Fouriertransform creates a suitable amplitude pattern on the retina formeasuring ocular distortion.28. The system of clause 26, wherein the amplitude pattern comprises oneor more of a grid pattern, concentric circles, or arrays of dots.29. The system of any one of clause 22-28, wherein the retinal imagingsystem comprises a fundus camera.30. A method for measuring retinal shape, comprising:displaying, to a subject, an image of a known distorted target patternhaving characteristic features and characterized in terms of size andshape relative to the distance away from the subject's eye;tracking the edge points of the distorted target pattern duringcorrection or un-distortion of the distorted target pattern by thesubject, until the characteristic features are undistorted to provide abaseline measure for the amount of distortion present in the subject'seye;placing a lens of a given power in front of the subject's eye;tracking the edge points of the distorted target pattern duringcorrection or un-distortion of the distorted target pattern by thesubject, until the characteristic features are undistorted to provide ameasure for the amount of distortion present in the subject's eye; anddetermining, using the baseline measure and the lens measure, a radiusand/or shape (e.g., conic shape) of the subject's retina (e.g., based onthe presumption that the amount of distortion changes equivalently for agiven retinal shape).31. The method of clause 30, wherein the distorted target patterncomprises a grid pattern having distorted lines, and wherein correctionor un-distortion of the distorted target pattern by the subjectcomprises correcting or un-distorting the distorted grid lines of thegrid until they are straight and square.32. The method of clause 30 or 31, wherein correction or un-distortionof the distorted target pattern by the subject and tracking thereof todetermine residual distortion and characteristics of retinal shape orasphericity from the corrected target comprises use of suitable software(e.g., image processing, mathematical fitting algorithms, tracking,alignment, etc.).

DETAILED DESCRIPTION OF THE INVENTION

According to particular aspects of the invention, distortion, ratherthan astigmatism, is a primary trigger for onset and progression ofmyopia, and systems and methods for measuring distortion in the eye andlens corrections that affect the eye's distortion are provided.

FIG. 27 shows a schematic summary of three basic functions relating tomeasurement of ocular distortion according to particular exemplaryaspects of the present invention. First, a known target pattern isprojected onto the retinal surface. Second, an image sensor captures theimage of the target on the retinal surface. Third, post processing oftarget fiducials back out useful information related to oculardistortion.

Particular aspects provide systems and methods for measuring distortionin the eye as a predictor of myopia, and lens corrections that affectthe eye's distortion are provided.

Exemplary systems include, but are not limited to measuring devicescomprising, for example, an adaptation to a traditional fundus camerasystem, or scanning methods, for the purpose of measuring or detectingdistortion in the human eye.

Exemplary systems (e.g., a modified fundus camera system) and methodsfor measuring ocular distortion comprise projecting an image of a knowntarget pattern having characteristic features onto an area of a retinalplane/surface to provide a distorted retinal image of the target patternacross the area of the retinal surface, recording the distorted retinalimage of the target pattern using an image sensor to provide a captureddistorted retinal image of the target pattern across the area of theretinal surface, identifying the characteristic features of the captureddistorted retinal image, and comparing the identified characteristicfeatures of the captured distorted retinal image of the target patternacross the area of the retinal surface to corresponding characteristicfeatures of the known target pattern to provide a map of oculardistortion across the area of the retinal surface.

Additional aspects provide methods for measuring retinal shape.

Distortion

There are several types of distortion that are possible. FIG. 1 shows awide range of these various types of distortion. Distortion createsnon-uniformity and a bending of straight-line objects or rectilinearobjects. In traditional optical systems, distortion can be introduced bydecentering or misaligning lens elements, tilting a detection plane orthrough manufacturing defects of lens elements. Furthermore, distortionis highly dependent on field of view and orientation, where imagingsystems with a wide field of view traditionally exhibit high amounts ofdistortion.

According to particular aspects of the present invention, the human eyemay experience a high amount of distortion that is corrected, at leastto some degree by the brain. Characterizing the amount and type ofdistortion present in the human eye has not been done before and, priorto Applicant's present disclosure, remained an unexplored topic withrespect to its effect on eye growth and development.

Particular aspects of the present invention provide a method formeasuring absolute distortion in the human eye as a new diagnostic tool.The methods provide, for example, for understanding and characterizationof normal eye growth in pediatric subjects, determining the onset orprogression of myopia, as well as a characterization method for retinalshape.

Particular aspects provide a measuring device comprising an adaptationto a traditional fundus camera system for the purpose of measuring ordetecting distortion in the human eye.

Systems

According to particular aspects of the invention, various opticalconfigurations (hardware/software) can be constructed to perform oculardistortion measurement. A modified fundus camera configuration formeasuring ocular distortion is one such example of a hardware/softwaresolution to this problem.

A fundus camera allows the operator to image the retina directly. Mostfundus camera design requirements and documentation is found In patentliterature (see, e.g., N. Shibata, and M. Torii, “Fundus Camera,” U.S.Pat. No. 6,654,553 (2003); N. Shibata, “Fundus Camera,” U.S. Pat. No.6,755,526 (2004); N. Kishida and S. Ono, “Eye Fundus ExaminationApparatus,” U.S. Pat. No. 7,055,955 (2006); N. Ichikawa, “FundusCamera,” U.S. Pat. No. 7,219,996 (2007); K. Matsumoto, “FundusImage-Taking Apparatus and Method,” U.S. Pat. No. 6,832,835 (2004); T.Nanjo and M. Kawamura, “Fundus Camera,” U.S. Pat. No. 574,274 (1998); Y.Sugina, T. Abe, T. Takeda and T. Kogawa, “Ophthalmologic PhotographingApparatus,” U.S. Pat. No. 7,147,328 (2004); Filipp V. Ignatovich, etal., “Portable Fundus Camera,” U.S. Pat. No. 8,836,778 (2014); PaulAndrew Yates, Kenneth Tran, “Hand-held portable fundus camera forscreening photography,” PCT Patent Application WO2011/029064). FIG. 2illustrates a schematic of a traditional fundus camera system fromZeiss.

A fundus camera consists of three main systems, which are anillumination system, an imaging system and fixation target that allshare a common optical path. The illumination system consists of one ormore sources (see, e.g., X and W in FIG. 2) with a series of condensingoptics (L9 and L1 in FIG. 2). Light travels through a glass plate P, toa field lens and aperture L2 before being directed up to a relay systemby mirror M1. Camera lenses L3 and L4 project light onto the surface ofmirror M2 which has a hole in it. This hole is projected into the planeof the pupil in the eye by an aspheric objective lens L5. The light isthen focused to the retinal plane of the eye to illuminate the retinalsurface. Light scattered from the retinal surface emerges from the eye,back into the camera system again through L5, projecting the image ofthe retina through the hole in M2. Lenses L6 and C combine with lenssystem L7 to relay the retinal image to a detector F or a monocularviewing plane at the end of lens system Lb. Lens system L7 is a reversetelephoto system that provides correction to any ocular aberration inthe form of defocus.

According to particular aspects of the present invention, the prior artsystem detailed in FIG. 2 is modified by placing a known pattern,capable of picking up distortion of the eye, in a plane between mirrorM1 and lens L3 that is conjugate to the retina. This element may be, butis not limited to, at least one element selected from the groupconsisting of a rectilinear grid pattern, concentric ring pattern(s), avariable density grid pattern, phase plate, diffractive optic, and/ornull correcting plate.

Alternatively, scanning systems can be constructed to perform oculardistortion measurement, wherein a beam is scanned over the surface ofthe retina in a raster pattern. Modulation of the beam at certainpositions in the scanning cycle provides for measurement of oculardistortion. For example, the beam may be turned off when scanning overknown grid positions and back on for regions not on the grid, and theresulting raster image will appear to have the projected targetsuperimposed on the retinal surface. Such a scanning method creates theknown grid pattern point by point during the scan and records thedeviation of each point on the retina to build the ocular distortionmap.

Method for Measuring the Amount and Type of Distortion

According to additional aspects, imaging the proposed pattern onto theretina and post processing the detector image provides for revealing theamount and type of distortion present in the eye. FIG. 3 shows,according to an exemplary embodiment, considerable complex distortionshown after grid projection into a human eye. Specifically, in FIG. 3, atransparent plate with a rectangular grid of dots was placed at thelocation between M1 and L3 (with reference to FIG. 2), and theillumination system effectively projected this grid pattern onto theretina. The image of FIG. 3 was captured through a custom mount used tocouple an eyepiece imaging path L8 (with reference to FIG. 2) to acamera cellphone for high resolution imaging. In further exemplaryembodiments, such images were captured with a digital camera placed atlocation F of FIG. 2. FIG. 3 shows the optic nerve (light gray oval inthe left of the image), retinal blood vessels and the dot patternprojected onto the retinal surface. Multiple people have been imaged ina similar manner and a variation between eyes of various refractiveerror, age, ethnicity, and gender have been observed.

Additional aspects provide a method (e.g., using a software package) toextract the amount and type of ocular distortion present in the humaneye. A detection of centroid signatures from the grid pattern are foundautomatically from frequency space image processing. A variablefrequency pass method is swept across the image to remove intensityvariations, suppress strong objects such as the optic disc and bloodvessels while boosting the distortion pattern to the foreground. Fromhere, centroid or corner detection is completed to identify various gridpositions across the retinal surface. With knowledge of the grid targetpattern a calculation of absolute distortion can be completed throughmathematical fitting. FIG. 4 shows distortion pattern detection usingthe method implemented with the point extraction software package.

To quantify the amount and type of distortion for each measured eye, amathematical fitting algorithm was developed that correlates an optimalgrid pattern on the retina, free from distortion, with the experimentaldistortion results. Calibration of the camera system for inherentdistortion allows for an absolutely measurement of ocular distortion.From this point, a map of ocular distortion across the retinal surfacecan be produced as seen in FIG. 5 where the red dots represent theundistorted pattern and the black dots are the ocular distortionpattern.

According to further aspects, the camera system distinguishes betweendistortion caused by camera misalignment (e.g., strong keystone effect)and distortion when aligned (e.g., local changes in retinal topography).The reduction of this distortion noise allows the diagnostic of globaland local retinal curvature to be extracted. Furthermore, combining eyerotation and continuous distortion mapping, a larger area and field ofretinal topography can be mapped and measured by using a stitchingalgorithm for several images.

The system and methods provide a means of objectively measuringdistortion in the eye, as well as monitoring temporal changes indistortion. Implementing this device and method into regular checkupsfor young children, for example, provides for the tracking of oculardistortion changes during critical years of eye growth. According to yetfurther aspects, therefore, watching the change or progression in thecontext of a distortion metric provides for detecting early onset myopiaor hyperopia in patients, and further provides for implementingintervention methods, e.g., corrective or control measures for oculardistortion such as spectacle lenses, contact lenses, multifocal lenses,and other modalities.

Method for Measuring Retinal Shape

Yet additional aspects provide a method for measuring retinal shape.With knowledge of the anterior and posterior corneal radii of curvature,the corneal thickness, the axial length of the eye, the anterior andposterior crystalline lens radii of curvature, and the crystalline lensthickness, it is possible to determine the retinal radius of curvatureand conic shape.

The method comprises displaying, to a subject, a known grid pattern interms of size and shape relative to the distance away from a subject'seye. The subject is asked to correct or un-distort the image such thatthe lines of the grid are straight and square. Tracking the edge pointsof the grid during this process provides the baseline for the amount ofdistortion present in the subject's eye.

Next, a lens of a given power is placed in front on the subject's eye.Again with a known grid pattern, the subject is asked to un-distort thegrid pattern and the changes are tracked. For any given eye in the humanpopulation, the amount of distortion changes equivalently for a givenretinal shape. Thus, information from the baseline and lens case can beused to back out the retinal radius and conic shape. FIG. 6, shows,according to particular exemplary aspects of the present invention,comparison of distortion between small and large retinal curvatures.

Example 1 A Target Pattern was Placed in the Illumination Path of aFundus Camera at a Location that is Conjugate to a Retina

This exemplary working example describes placing a target pattern in theillumination path of a modified fundus camera at a location that isconjugate to a retina.

FIG. 7 shows a source image and annulus mask at location 3 that areconjugate to a holed mirror at location 8. The dot grid pattern atlocation 5 is picked up along the illumination path and is conjugate tothe retina by the holed mirror (red). A series of aspheric condensinglenses compress and image the illumination through a glass plate atlocation 2 to an intermediate image plane at location 3, where a set ofadjustable apertures lie. There is a second opening located above theglass plate where a second source can be placed in the illuminationpath. Given that the fundus camera uses an internal illumination scheme,the critical annulus used to eliminate corneal back reflections isplaced at location 3 where the intermediate source image is formed. Afirst modification to the fundus camera may be replacing, e.g., a 60 Wincandescent bulb source with three 1 W LED's from CREE at location 1 toprovide equivalent light throughput for illuminating the retinalsurface. A piece of ground glass may be placed in front of the LED suiteto create a diffuse light source.

As illustrated in FIG. 7, the second arm of the illumination pathway ofthe modified fundus camera creates an intermediate image of the sourceand annulus at a conjugate plane with a holed mirror seen at location 8.After a being redirected by a fold mirror at location 4, a secondmodification to the fundus camera, a grid pattern for measuring oculardistortion, is picked up by the illumination path at location 5. Thegrid pattern at location 5 is conjugate to the retinal surface, orequivalently, the location of the intermediate image of the retina,which forms before the holed mirror at location 5 and is shown in red.The legacy (prior art) system has a small fixation target located at thesame location as plane 5 which was used to fix a subject's gaze duringmeasurement and served as the intended target plane location. Objectiveelements at location 6 complete the relay path to the holed mirror.There is a central black dot obscuration at location 7 that is conjugateto the back surface of the aspheric objective lens. The obscurationeliminates the back reflections from the two objective surfaces fromentering the imaging path.

The grid target used in this exemplary body of work was chosen to be arectilinear grid of dots with a diameter of 0.5 mm and spacing of 1 mm.The actual dot diameter and spacing was measured on a Zygo NewView 8300interferometer, used primarily as a microscope in this case. The truedot diameter is approximately 0.642 mm and spacing of 1 mm. A piece ofglass supported the grid target and both were fixed to a 3D printedmount. The mount arm fixed to the existing body of the fundus camerathrough a set of three screws and was fixed in place at location 5. Thetarget was aligned using a model eye on an optical bench and the Zlocation corresponding to the distance away from the fold mirror atlocation 4 was determined by imaging an emmetropic subject and findingthe plane of best focus for the grid pattern. FIG. 8 shows the mountingscheme for the grid target; a 3D printed cover and grid target holder.The grid target, fixed to a glass plate, is located inside the redcircle of the image and placed conjugate to the intermediate retina.

FIG. 9 illustrates, according to particular exemplary aspects of thepresent invention, the retinal illumination and return path of themodified fundus camera. The holed mirror image at location 8 isconjugate to the pupil of the eye located at 11. An intermediate imageof the retina with the grid target projection becomes the objectpresented to the imaging pathway for detection (red). The grid patternprojection onto the retina is accomplished by making the holed mirrorconjugate to the pupil plane of the eye at location 11. A baffle atlocation 9 helps to further control the corneal back reflectionsreturned into the imaging path way. The aspheric objective lens atlocation 11 serves a critical role in the fundus camera design. A fastobjective, approximately f/2 is responsible for bringing the annulus oflight into focus at the pupil of the eye. A uniform illumination coversthe retinal surface and effectively creates a virtual object of theretina and grid pattern at infinity to be recorded by the imagingpathway (133).

The retinal surface can be considered a Lambertian scatterer (134) thathas different reflectance values for wavelengths in the visual band,with red light having the highest value around 40% (135). Thus, a strongillumination source is required to have sufficient intensity of theretinal image compared to return signals from unwanted surfaces such asthe anterior cornea. The light exiting the eye is telecentric passinginto the aspheric objective. Here the objective must flatten thecurvature of the retina to ensure plane to plane imaging. Pomerantzeff,et. al., illustrates the difficulty in flattening the curvature of theretina for large angles to a common flat focus plane (133). Therefore,careful attention to the optical design of such an objective as well asadditional lens components may be used to further improve to the legacyfundus imaging system.

FIGS. 10-14 show, according to particular exemplary aspects of thepresent invention, seventeen (17) retinal images with the projected gridpattern from cohort subjects. Self-reported refractive error is listedabove each image.

FIGS. 10-14 demonstrate qualitative answers to two questions. Given thatthe location of the grid pattern is conjugate to the retina in thefundus camera system, it is indeed possible to resolve reasonably sharptarget features for distortion measurement in human eyes. Second, byvisual inspection, there appear to be differences between hyperopic,emmetropic, and myopic individuals in both ocular distortion patternsand overall image resolution of the grid pattern. FIG. 11 demonstratesthis difference most clearly, where the +2 D subject appears to havemore barrel distortion in the grid pattern than the more pincushion 0 Dsubject in the sub image above. The patterns of emmetropic and myopicsubjects tend to show a more pincushion effect toward the periphery, butin some cases show a collapsing together of points in the centralregion. FIG. 12 demonstrates this effect of point collapse in the centerfor the −0.5 D subject and compared to the 0 D subject in the sub imagebelow. Motivation for expanding the wavefront representation out tohigher orders lies in reviewing the experimental data and seeing thesecombinations of distortion terms, creating complex distortion patterns.

Example 2 An Exemplary Target Consisted of a Known Pattern that ContainsUseful Fiducials for Quantifying Ocular Distortion

This exemplary working example illustrates use of a known grid patternthat contains useful fiducials for quantifying ocular distortion.Examples of such targets include, but are not limited to, a gridpattern, concentric circles, arrays of dots, etc.

FIGS. 10-14 show, according to particular exemplary aspects of thepresent invention, seventeen (17) retinal images with the projected gridpattern from cohort subjects. Self-reported refractive error is listedabove each image.

Example 3 A Phase Target May be Placed in the Illumination Path of aFundus Camera at a Location that is a Fourier Transform Plane of theRetina

The systems and methods may comprise placing a phase target in theillumination path of a fundus camera at a location that is a Fouriertransform plane of the retina, wherein the phase target may have asuitable pattern (e.g., grid pattern, concentric circles, arrays ofdots, etc.) such that its Fourier transform creates a suitable amplitudepattern on the retina for measuring ocular distortion.

Example 4 An Image Sensor in the Imaging Path was Used to Record Imagesof the Target Projected onto the Retina

FIG. 15, shows the final imaging path for the modified fundus camera. Anintermediate image of the retinal surface and grid target is locatedbefore holed mirror at location 8. The location of this intermediateimage is different for subjects with any refractive error. A zoom lenssystem corrects for any added power induced by the eye under test. FIG.15 illustrates the final optical path that relays the intermediate imageof the retinal surface to a detection plane at location 13. The imagingpath, shown in orange, consists of a zoom lens configuration marked bylocation 12. Various degrees of ametropia will cause the intermediateimage of the retina to form at different points along the optical axisin front of the holed mirror. The retinal image is passed through aseries of lenses to correct the ametropia of the eye including twoastigmatic lenses that combine to form a cylindrical lens of variableaxis and power to correct for patient astigmatism (131). From thecorrection lenses the object pattern is sent to a doublet that begins atypical zoom lens system. Changing the spacing between the doublet,negative lens and positive lens, it is possible to compensate for thevaried axial position of the intermediate retinal image. Thus, the focallength to the detection plane is altered to present a sharp image of theretina at the detection plane.

Example 5 Real-Time Image Processing Software that Examines Features inthe Retinal Image Such as Blood Vessels was Used to Provide Autofocus ofthe Image and to Ensure Alignment of the Eye Between Consecutive Images;Blood Vessels were Kept in Focus and Tracked Over Subsequent Images

To quantitatively show that there is variability between hyperopic,emmetropic, and myopic subjects, a testing criterion was created for thedata processing of the grid pattern centroids.

First, each image selected for processing required the optic disc toappear on the right side of the image. Given that the right eye for eachsubject under test was used, this provides a roughly equivalent retinalarea for investigation for each subject. In the case of the +2 Dsubject, whose left eye was imaged, the image was rotated about thevertical axis to place the optic disc on the right side of the image.

Second, three images of each subject are processed to create a mean forthe fit distortion values.

Third, in each of the three images, the eye must not have rotated morethan 3.5° between subsequent images.

The approximate field of view of the fundus camera is around 50° or 2020pixels (e.g., on a cellphone sensor). By tracking a portion of a bloodvessel in each of the selected images, the average pixel movement of theeye was recorded. Therefore, images where blood vessel jumps were lessthan 175 pixels and met the criterion of optic disc location, wereselected as processing candidates for distortion fitting. A sample setof three images is shown in FIG. 16.

FIG. 16, shows, according to particular exemplary aspects of the presentinvention, three processing candidate images that meet the criterion forpost-processing. It is of important note that the pattern does notdeviate significantly even with small eye rotations, supporting that astable, repeatable target can be imaged on the retina.

Example 6 Software Guided Alignment Assistance was Useful with EyeTracking

One of the largest errors experienced in human imaging comes from cameraand eye misalignment. Particular aspects of the present invention,provide for modifying a second-generation camera system with a morerobust alignment package. For example, a minimally invasive eye trackingcamera setup may be placed on the front of the system. Using nearinfrared (NIR) radiation, the pupil of the eye can be monitored, and thecenter of the pupil tracked using, e.g., the starburst method as oneexample of many eye tracking techniques. Capturing the eye in the NIRprovides a high contrast pupil boundary for image processing while alsominimizing ambient light noise during fundus imaging. Tracking pupilgaze direction allows for some eye orientation information andconsequently, retinal area information.

Additional aspects, provide for monitoring the fundus camera bodylocation relative to the head or chin rest position. For example,monitoring relative distance away from these fixed datums providesuseful 3D spatial information of the camera pointing direction relativeto gaze direction. Using a model eye on a laboratory bench, the systemis calibrated with a known grid target pattern projected onto theretinal model eye. Translation stages and rotation stages placed at thecenter of rotation of the model eye are then used to displace the modeleye in varying degrees while images of the target pattern are captured.After several runs, a database of ocular distortion images is capturedand in human imaging, the database is accessed to correlate live imageswith eye model patterns. When the camera system identifies forms thatare likely due to misalignment, image capture is functionality turnedoff to reduce the number of problematic or high error distortionpatterns, flagged in the data set, or have a compensating mechanismwhich drives the system back into alignment.

Finally, auto detection suites may be implemented to capture andidentify retinal features. By identifying retinal features such as bloodvessels, the camera system can autofocus to ensure that image capture ofthe retinal surface is always well resolved. Tracking of these featuresmay also ensure alignment by tracking the movement of features betweensubsequent images. If the eye rotates too far, or the head shifts duringimaging, the software may flag images taken during these large movementsor stop image capture functionality.

Example 7 Hardware and Software was Used to Track the Human Eye and GazeDirection, Ensuring Eye Alignment, and Providing Useful Information onEye Orientation During Measurement of Ocular Distortion

FIG. 17, shows, according to particular exemplary aspects of the presentinvention, that lateral misalignment with respect to the eye stop causeskeystone distortion. Example images for the calibration system (top) andin a human subject (bottom). In the human subject, the corner oppositethe vignetting side appears to pinch and blur.

Recognizable patterns can indicate misalignment for correction, usingthe hardware and software described in Examples 5 and 6.

Example 8 Software was Used to Analyze the Captured Images andAutomatically Identify Features in the Target Pattern, which IdentifiedFeatures were then Compared to the Known Target Patter to Quantify theAmount of Ocular Distortion

As discussed in Example 5, to quantitatively show that there isvariability between hyperopic, emmetropic, and myopic subjects, atesting criterion was created for the data processing of the gridpattern centroids. First, each image selected for processing requiredthe optic disc to appear on the right side of the image. Given that theright eye for each subject under test was used, this provides a roughlyequivalent retinal area for investigation for each subject. In the caseof the +2 D subject, whose left eye was imaged, the image was rotatedabout the vertical axis to place the optic disc on the right side of theimage. Second, three images of each subject are processed to create amean for the fit distortion values. Third, in each of the three images,the eye must not have rotated more than 3.5° between subsequent images.

The approximate field of view of the fundus camera is around 50° or 2020pixels (e.g., on a cellphone sensor). By tracking a portion of a bloodvessel in each of the selected images, the average pixel movement of theeye was recorded. Therefore, images where blood vessel jumps were lessthan 175 pixels and met the criterion of optic disc location, wereselected as processing candidates for distortion fitting. A sample setof three images is shown in FIG. 16.

The data processing scheme to find the grid pattern centroids and fit todistortion wavefront errors follows a series of post processing stepsthat are completed using MATLAB numerical software. Processing dotcentroids in, e.g., the human subject images is done in two steps, anautomatic Fourier based method and a user defined clicking procedure.

A candidate image was loaded, cropped and resized to perform acomputationally efficient Discrete Fourier Transform (DFT). Pixelcoordinate space was transformed to real space with knowledge of, e.g.,the cellphone sensor parameters. Similarly, the Fourier or frequencyspace domain was created from the real space coordinates and inconsideration with Nyquist sampling theorem. A complex filteringfunction was incorporated in the raw image DFT to reduce intensityvariation and suppress noise artifacts found on the retina.

FIG. 18 shows, according to particular exemplary aspects of the presentinvention, a filtered Fourier Transform (FT) shown on the left with thebinary resultant image of the retina shown on the right.

The Fourier domain shows the frequency separation of the grid patternrelated to the dot pitch, which is the critical information necessary tocalculate centroid center locations. Uneven illumination, color, andnoise artifacts such as blood vessels can be suppressed relatively wellthrough this method. In this exemplary instance optimization of thecomplex filter function was not performed as each subject case hadvaried levels of dot image quality. Furthermore, no image enhancementrelated to dot shape or size was performed due to the variability inresolution capability for each subject. Once the binary image wasformed, an internal MATLAB algorithm was used to identify centroidlocations. Contiguous matrices used a nearest neighbor approach toidentify connected components in the binary image. Careful selection ofcomponent size yields locations of dot centers as shown in FIG. 19.Approximately, 25%-50% of image points can be captured automaticallysign this automated method.

FIG. 19 shows, according to particular exemplary aspects of the presentinvention, centroid centers marked in red that were found by theinternal centroid algorithm. Incorrect or misinterpreted centroid valuesare also present in the image and indicate false positive centers. Thefalse positives for centroids seen in FIG. 19 provide the motivation forthe second centroid center locating procedure, user hand clicked points.While automatic detection relies on mathematical weighting do determinecentroids, the reality of false positives requires the aid of the humaneye. While also correcting for these erroneous centroid locations, it ispossible to expand the data set by user clicking the remaining centersfor increased point sampling in the image. Once the final centercoordinate location has been recorded, points are passed through asorting algorithm to orient point (1,1) in the matrix as the upper leftmost point all the way through point (11,11). This ensures that properfield coordinates can be identified.

Typically, distortion values are reported such that the distorted imageor target is referenced to a nominal or undistorted object. To create anominal or undistorted reference grid, where the center location ofcentroids should have been located, a center spacing value wascalculated for each image processed. The spacing between the center dotof the grid pattern at position (6,6) and the four nearest neighborswere averaged to create the nominal grid spacing, centered at the (6,6)position and illustrated in FIG. 20. The assumption made for thereference grid is that the distortion around the (6,6) point should below given that this is close to the optical axis of the fundus camerasystem.

FIG. 20 shows, according to particular exemplary aspects of the presentinvention, that the method for determining the undistorted referencegrid is done by taking the center dot of the grid pattern at (6,6) andaverage the four nearest neighbor distances seen in dark gray. Removedfalse-positive points (white) can be viewed with the automatically foundcenters (light) and the user hand-clicked centers (dark gray).

The center hand clicking method can raise concerns of error in centerlocation values. User bias, accuracy, fatigue and image noise allcontribute to potential error. To understand the type of error thatcould be induced during the hand clicked processing a repeatability testwas devised. Using a nominal image like the one shown in FIG. 20, theautomated and manual processing was completed back to back six times. Ineach of the six runs, the number of automatically found points wasincreased or decreased to see if there was influence on having amajority of the found centers come from hand clicking.

FIG. 21 (Table 1) shows/reports repeatability results for automatic andmanual center dot processing, and lists the number of automaticallyfound points and the center spacing of the resulting distorted centerlocations. All number are in units of pixel space coordinates. It wasdetermined that the two-stage process of center location was sufficientfor processing the entirety of the human retina dataset.

Coefficient Fitting and Results. With the assumption that the human eyeis a rotationally non-symmetric optical system and using the wavefrontexpansion from Barakat, the processed centers from each subject was fitto 4^(th) order distortion coefficients in x and y. Though this textextends the wavefront error out to the 6^(th) order, it was found thatthe least squares fitting was over constrained causing numerical errorin lower fit orders.

Using the nominal spacing value to determine nominal grid coordinates(x_(o),y_(o)) a polynomial matrix A, is formed to evaluate the distortedcenter coordinates (x,y). The nominal grid points (x_(o),y_(o)) and thedistorted grid points (x,y) share a common center point which is(x₆,y₆). Subtracting the center point from both sets of coordinatescreates a Cartesian pixel space of positive and negative coordinates.The following least-squares minimization equation to find the distortioncoefficients for (x,y) is shown in Equation 4.1 below.

$\begin{matrix}{{A_{x} \smallsetminus b_{x}} = {{\begin{bmatrix}x_{o,1} & y_{o,1} & x_{o,1}^{2} & \text{…} & x_{o,1}^{3} \\\vdots & \vdots & \vdots & \ddots & \vdots \\x_{o,n} & y_{o,n} & x_{o,n}^{2} & \text{…} & x_{o,n}^{3}\end{bmatrix}\begin{bmatrix}x_{1} \\\vdots \\x_{n}\end{bmatrix}} = {{\begin{bmatrix}F_{x,1} \\\vdots \\F_{x,9}\end{bmatrix}{A_{y} \smallsetminus b_{y}}} = {{\begin{bmatrix}x_{o,n} & y_{o,1} & x_{o,1}^{2} & \text{…} & x_{o,n}^{3} \\\vdots & \vdots & \vdots & \ddots & \vdots \\x_{o,n} & y_{o,n} & x_{o,n}^{2} & \text{…} & x_{o,n}^{3}\end{bmatrix}\begin{bmatrix}y_{1} \\\vdots \\y_{n}\end{bmatrix}} = \begin{bmatrix}F_{y,1} \\\vdots \\F_{y,9}\end{bmatrix}}}}} & 4.1\end{matrix}$

In Equation 4.1, n is equal to the number of dot centers found, with amaximum of 121 found centers for the 11×11 target grid but the number ofpoints varies between subjects. F_(x,y) represent the x and y distortionterms for the 4^(th) order wavefront error expansion. These F_(x,y)coefficients represent the Barakat B-D labeling coefficients and will bereported as such. Thus, 18 independent coefficients are fit in thisprocess.

The 10^(th) subject of this study was chosen at random do demonstratethe variance in coefficient values across the three processed images.The coefficient values for each processed image for all 18 coefficientsare shown in FIGS. 22-24.

FIG. 22 shows, according to particular exemplary aspects of the presentinvention, linear B coefficient values for human subject 10. Note B₂ andB₃ correspond to x and y linear magnification respectively. Fit valuesare stable and of reasonable magnitude for each processed image.

FIG. 23 shows, according to particular exemplary aspects of the presentinvention, quadratic C coefficient values for subject 10. Fit values for(C₇, C₈, C₉, C₁₃) are stable and of reasonable magnitude for eachprocessed image. Fit values for (C₁₂, C₁₄) are unstable in sign andmagnitude making them inconclusive representations of image distortion.

FIG. 24 shows, according to particular exemplary aspects of the presentinvention, Cubic D coefficient values for subject 10. Fit values for(D₂₃, D₂₆, D₂₇, D₂₈, D₃₀) are stable and of reasonable magnitude foreach processed image. Fit values for (D₂₄, D₂₅, D₂₉) are unstable insign and magnitude making them inconclusive representations of imagedistortion.

While over the three processed images of FIGS. 22-24, some coefficientsflip sign or span zero, and the trustworthiness of these coefficients atrepresenting their corresponding distortion of the subjects is thereforeinconclusive, most coefficients nonetheless appear to be stable acrossthe three images, bringing confidence to the fitting as well assupporting that representing complex distortion of the eye as componentsis be a valid approach in understanding ocular distortion.

From the three images processed for each subject, the mean and standarddeviation of each coefficient was found. Mean values for each subjectare plotted against their self-reported refractive error. Given thesystem sensitivity and low number of processed images, only fourdistortion terms (D₂₃, D₂₆, D₂₈, D₃₀) related to building 3^(rd) orderradial distortion are discussed for the entire subject population. Itshould be noted that beyond the four terms, a few other distortion termvalues exhibited potential trends with respect to refractive error.

For the D₂₆ and D₂₈ coefficients, no significant trend occurred in themean data set across the population. The D₂₃ coefficients exhibited andinteresting nature of two pooled value groups, one around −3e⁻⁰⁷ andanother around −6.5e⁻⁰⁷. This distortion coefficient can be thought ofas an increase in point spacing in the nasal-temporal meridian, whereseparation is largest at the maximum field extent. Lastly, D₃₀coefficients exhibited a grouping around a value of −5e⁻⁰⁸, which wouldsuggest a population centered around some common level of barreldistortion in the nasal-temporal meridian.

The next logical step combines the four distortion terms above into arelative value of radial distortion. In most literature and in practice,radial distortion is calculated as a percentage in the form of adistance ratio of the residual movement. The distance from a centerlocation to the nominal location of the maximum field point is comparedto the distance from center of the distorted maximum field coordinate.Equation 4.2 below describes the formula used to calculate the percentdistortion, where r=√{square root over (x²−y²)}.

$\begin{matrix}{{\% D} = {\frac{r_{distorted} - r_{nominal}}{r_{nominal}}*100}} & 4.2\end{matrix}$

The nominal spacing for each run was used to build the nominal gridpoints. The mean of (D₂₃, D₂₆, D₂₈, D₃₀) was, applied to the 4^(th)order wavefront error equation with (B₂=B₃=1) and the nominal grid pointas seed (x_(o),y_(o)) coordinates. The maximum field coordinate alongthe diagonal of the square grid of points was used to calculate theradial distortion percentage. A plot of the mean percent distortion forthe entire refractive population is shown in FIGS. 25A and 25B, alongwith the percent distortion found from the simulated population.

FIGS. 25A and B show, according to particular exemplary aspects of thepresent invention, mean radial distortion as a percentage plottedagainst the subject refractive error (FIG. 25A) and the simulatedpopulation radial distortion plotted against the subject refractiveerror (FIG. 25B). The square □ and diamond ⋄ markings indicate stronglyresolved and weak or blurred dot patterns respectively. The markers inblack correspond to subjects who had one or more radial distortioncoefficient values span zero, indicating inconclusive results.

The amount of radial distortion for the population appears to be around−0.5% barrel distortion after the 0.5% pincushion camera distortionoffset is applied from calibration. Subjects who had one or more oftheir radial distortion terms be both positive and negative over thethree processed images, were found in subjects of higher levels ofmyopia, perhaps reflecting limitations of an off the shelf cameraconfiguration in measuring ocular distortion for individuals of highermyopia. Nonetheless, the data set from the human trials appears to matchthat of the simulated population.

Two new postulates can be raised from inspection of the radialdistortion in FIGS. 25A and 25B. First, it may be that the eye grows,reshaping ocular components, to reach zero radial distortion or someminute value of barrel distortion in older age. Given that humans see a3D world in only 2D, perhaps a similar compensation to null distortionis occurring in the brain for flat plane imaging as this fundus camerasystem does, where distortion values slightly out of some threshold setinto motion mechanisms for eye growth. Second, the role of retinalcurvature may play an important role in local distortion and visualperception. The variability of retinal curvatures coupled with thevariability in ocular components could be an indicator for the dataspread seen in the emmetropic and hyperopic subjects. Perhaps, the size,shape, and spacing of ocular components for each individual needs toreach minimum level for that given individual. It may be the case thatsome individuals can tolerate higher or lower levels of oculardistortion without causing eye growth leading to refractive development.

To evaluate the effectiveness of least squares fitting of distortionpoints to the 4^(th) order wavefront error function, a series of figuresas well as RMS distance error was calculated for the fit points of allsubjects. The 2^(nd) order fit, rebuilds the distorted wavefront pointsusing only the linear B coefficients and nominal grid point locations.The 3^(rd) order fit and 4^(th) order fit are found using the sameprocedure but with the quadratic C coefficients and cubic D coefficientsrespectively.

The convergence was quite strong at the 4^(th) order wavefront error,leaving only higher frequency shifts present at certain field locations.This high order distortion is likely caused by local retinal curvaturedeviations. Though the representative coefficients for the 5^(th) and6^(th) order wavefront error were calculated, it was discovered that thefitting was not numerically stable at these orders. Increasing thenumber of sampling points on the grid would potentially allow ofnumerical stability in the least squares fitting to higher orders,capturing the high frequency distortion. Transitioning to a normalizedcoordinate space may also improve numerical stability.

A numerical measure of fit was performed for all subjects related to thedistance separation in pixels of fit points and distorted points. Theresidual distance between these two coordinates is reported as mean RMSerror described by Equation 4.3 and shown graphically in FIG. 26. Allsubjects monotonically decrease as fit order increases with somesubjects fitting much stronger than others. The deviation betweensubjects is related to the uncertainty in determining dot location dueto blurred or weak dots, low resolved distortion point count, orpresence of high order distortion beyond the 4^(th) order.

$\begin{matrix}{{{RMS}\mspace{14mu}{Fit}\mspace{14mu}{Error}} = \sqrt{\frac{\sum\sqrt{\left( {x_{fit} - x_{distort}} \right)^{2} + \left( {y_{fit} - y_{distort}} \right)^{2}}}{\sharp\mspace{14mu}{of}\mspace{14mu}{points}\mspace{14mu}{found}}}} & 4.3\end{matrix}$

FIG. 26 shows, according to particular exemplary aspects of the presentinvention, mean RMS Fit error for all subjects. All subjects seeimproved fitting out the 4^(th) order wavefront error. Error units arein pixels. Larger error is attributed to weak dot contrast, low numbersof points found, and higher order distortion.

Computing Device

FIG. 28 is a block diagram of hardware and an operating environment inconjunction with which implementations of the disclosed methods formeasuring ocular distortion may be practiced. The description of FIG. 28is intended to provide a brief, general description of suitable computerhardware and a suitable computing environment in which implementationsmay be practiced. Although not required, implementations are describedin the general context of computer-executable instructions, such asprogram modules, being executed by a computer, such as a personalcomputer. Generally, program modules include routines, programs,objects, components, data structures, etc., that perform particulartasks or implement particular abstract data types.

Moreover, those of ordinary skill in the art will appreciate thatimplementations may be practiced with other computer systemconfigurations, including the mobile communication device 300 (see FIG.8), hand-held devices, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Implementations may also be practiced indistributed computing environments (e.g., cloud computing platforms)where tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

The exemplary hardware and operating environment of FIG. 28 includes ageneral-purpose computing device in the form of the computing device 12.By way of non-limiting examples, the computing device 12 may beimplemented as a laptop computer, a tablet computer, a web enabledtelevision, a personal digital assistant, a game console, a smartphone,a mobile computing device, a cellular telephone, a desktop personalcomputer, a blade computer, and the like.

The computing device 12 includes a system memory 22, the processing unit21, and a system bus 23 that operatively couples various systemcomponents, including the system memory 22, to the processing unit 21.There may be only one or there may be more than one processing unit 21,such that the processor of computing device 12 includes a singlecentral-processing unit (“CPU”), or a plurality of processing units,commonly referred to as a parallel processing environment. When multipleprocessing units are used, the processing units may be heterogeneous.Byway of a non-limiting example, such a heterogeneous processingenvironment may include a conventional CPU, a conventional graphicsprocessing unit (“GPU”), a floating-point unit (“FPU”), combinationsthereof, and the like.

The computing device 12 may be a conventional computer, a distributedcomputer, or any other type of computer.

The system bus 23 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. The system memory22 may also be referred to as simply the memory, and includes read onlymemory (ROM) 24 and random access memory (RAM) 25. A basic input/outputsystem (BIOS) 26, containing the basic routines that help to transferinformation between elements within the computing device 12, such asduring start-up, is stored in ROM 24. The computing device 12 furtherincludes a hard disk drive 27 for reading from and writing to a harddisk, not shown, a magnetic disk drive 28 for reading from or writing toa removable magnetic disk 29, and an optical disk drive 30 for readingfrom or writing to a removable optical disk 31 such as a CD ROM, DVD, orother optical media.

The hard disk drive 27, magnetic disk drive 28, and optical disk drive30 are connected to the system bus 23 by a hard disk drive interface 32,a magnetic disk drive interface 33, and an optical disk drive interface34, respectively. The drives and their associated computer-readablemedia provide nonvolatile storage of computer-readable instructions,data structures, program modules, and other data for the computingdevice 12. It should be appreciated by those of ordinary skill in theart that any type of computer-readable media which can store data thatis accessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices (“SSD”), USB drives, digital videodisks, Bernoulli cartridges, random access memories (RAMs), read onlymemories (ROMs), and the like, may be used in the exemplary operatingenvironment. As is apparent to those of ordinary skill in the art, thehard disk drive 27 and other forms of computer-readable media (e.g., theremovable magnetic disk 29, the removable optical disk 31, flash memorycards, SSD, USB drives, and the like) accessible by the processing unit21 may be considered components of the system memory 22.

A number of program modules may be stored on the hard disk drive 27,magnetic disk 29, optical disk 31, ROM 24, or RAM 25, including theoperating system 35, one or more application programs 36, other programmodules 37, and program data 38. A user may enter commands andinformation into the computing device 12 through input devices such as akeyboard 40 and pointing device 42. Other input devices (not shown) mayinclude a microphone, joystick, game pad, satellite dish, scanner, touchsensitive devices (e.g., a stylus or touch pad), video camera, depthcamera, or the like. These and other input devices are often connectedto the processing unit 21 through a serial port interface 46 that iscoupled to the system bus 23, but may be connected by other interfaces,such as a parallel port, game port, a universal serial bus (USB), or awireless interface (e.g., a Bluetooth interface). A monitor 47 or othertype of display device is also connected to the system bus 23 via aninterface, such as a video adapter 48. In addition to the monitor,computers typically include other peripheral output devices (not shown),such as speakers, printers, and haptic devices that provide tactileand/or other types of physical feedback (e.g., a force feedback gamecontroller).

The input devices described above are operable to receive user input andselections. Together the input and display devices may be described asproviding a user interface. The user interface may be configured todisplay various screens and dashboards and receive input entered intoany of such screens.

The computing device 12 may operate in a networked environment usinglogical connections to one or more remote computers, such as remotecomputer 49. These logical connections are achieved by a communicationdevice coupled to or a part of the computing device 12 (as the localcomputer).

Implementations are not limited to a particular type of communicationsdevice. The remote computer 49 may be another computer, a server, arouter, a network PC, a client, a memory storage device, a peer deviceor other common network node, and typically includes many or all of theelements described above relative to the computing device 12. The remotecomputer 49 may be connected to a memory storage device 50. The logicalconnections depicted in FIG. 28 include a local-area network (LAN) 51and a wide-area network (WAN) 52. Such networking environments arecommonplace in offices, enterprise-wide computer networks, intranets andthe Internet. The network may be implemented using one or more of theLAN 51 or the WAN 52 (e.g., the Internet).

Those of ordinary skill in the art will appreciate that a LAN may beconnected to a WAN via a modem using a carrier signal over a telephonenetwork, cable network, cellular network, or power lines. Such a modemmay be connected to the computing device 12 by a network interface(e.g., a serial or other type of port). Further, many laptop computersmay connect to a network via a cellular data modem.

When used in a LAN-networking environment, the computing device 12 isconnected to the LAN 51 through a network interface or adapter 53, whichis one type of communications device. When used in a WAN-networkingenvironment, the computing device 12 typically includes a modem 54, atype of communications device, or any other type of communicationsdevice for establishing communications over the wide area network 52,such as the Internet. The modem 54, which may be internal or external,is connected to the system bus 23 via the serial port interface 46. In anetworked environment, program modules depicted relative to the personalcomputing device 12, or portions thereof, may be stored in the remotecomputer 49 and/or the remote memory storage device 50. It isappreciated that the network connections shown are exemplary and othermeans of and communications devices for establishing a communicationslink between the computers may be used.

The computing device 12 and related components have been presentedherein by way of particular example and also by abstraction in order tofacilitate a high-level view of the concepts disclosed. The actualtechnical design and implementation may vary based on particularimplementation while maintaining the overall nature of the conceptsdisclosed.

In some embodiments, the system memory 22 stores computer executableinstructions (e.g., for all or portions of ocular distortion measurementmethod(s)) that when executed by one or more processors cause the one ormore processors to perform all or portions of the method as describedabove. The system memory 22 may also store the dataset(s). Suchinstructions and/or dataset(s) may be stored on one or morenon-transitory computer-readable or processor readable media.

Mobile Communication Device

FIG. 29 is a functional block diagram illustrating the mobilecommunication device 300 that may be used to implement one or more ofthe computing devices of the system. The mobile communication device 300may be implemented as a cellular telephone, smart phone, a tabletcomputing device, and the like. By way of a non-limiting example, themobile communication device 300 may be implemented as a smartphoneexecuting IOS or Android OS.

The mobile communication device 300 includes a central processing unit(“CPU”) 310. Those skilled in the art will appreciate that the CPU 310may be implemented as a conventional microprocessor, applicationspecific integrated circuit (ASIC), digital signal processor (DSP),programmable gate array (PGA), or the like. The mobile communicationdevice 300 is not limited by the specific form of the CPU 310.

The mobile communication device 300 also contains the memory 312. Thememory 312 may store instructions and data to control operation of theCPU 310. The memory 312 may include random access memory, ready-onlymemory, programmable memory, flash memory, and the like. The mobilecommunication device 300 is not limited by any specific form of hardwareused to implement the memory 312. The memory 312 may also be integrallyformed in whole or in part with the CPU 310.

The mobile communication device 300 also includes conventionalcomponents, such as a display 314, a keypad or keyboard 316, and acamera or video capture device 318. For example, the display 314 may beimplemented as conventional touch screen display. These are conventionalcomponents that operate in a known manner and need not be described ingreater detail. Other conventional components found in wirelesscommunication devices, such as USB interface, Bluetooth interface,infrared device, and the like, may also be included in the mobilecommunication device 300. For the sake of clarity, these conventionalelements are not illustrated in the functional block diagram of FIG. 29.

The display 314, the keyboard 316, and the camera or video capturedevice 318 are operable to receive user input and selections. Togetherthe input and display devices may be described as providing a userinterface. The user interface is configured to display the variousscreens and dashboards described above and receive input entered intoany of these screens.

The mobile communication device 300 also includes a network transmitter322 such as may be used by the mobile communication device 300 fornormal network wireless communication with a base station (not shown).FIG. 8 also illustrates a network receiver 320 that operates inconjunction with the network transmitter 322 to communicate with thebase station (not shown). In a typical embodiment, the networktransmitter 322 and network receiver 320 are implemented as a networktransceiver 326. The network transceiver 326 is connected to an antenna328. Operation of the network transceiver 326 and the antenna 328 forcommunication with a wireless network (not shown) is well-known in theart and need not be described in greater detail herein.

The mobile communication device 300 may also include a conventionalgeolocation module (not shown) operable to determine the currentlocation of the mobile communication device 300.

The various components illustrated in FIG. 29 are coupled together bythe bus system 330. The bus system 330 may include an address bus, databus, power bus, control bus, and the like. For the sake of convenience,the various busses in FIG. 8 are illustrated as the bus system 330.

The memory 312 may store instructions for the ocular distortionmeasurement method(s) executable by the CPU 310. When executed by theCPU 310, the instructions may cause the CPU 310 to perform to performall or portions of the method(s) as described above. The memory 312 (seeFIG. 8) may also store the dataset(s). Such instructions and/ordataset(s) may be stored on one or more non-transitory computer orprocessor readable media.

The foregoing described embodiments depict different componentscontained within, or connected with, different other components. It isto be understood that such depicted architectures are merely exemplary,and that in fact many other architectures can be implemented whichachieve the same functionality. In a conceptual sense, any arrangementof components to achieve the same functionality is effectively“associated” such that the desired functionality is achieved. Hence, anytwo components herein combined to achieve a particular functionality canbe seen as “associated with” each other such that the desiredfunctionality is achieved, irrespective of architectures or intermedialcomponents. Likewise, any two components so associated can also beviewed as being “operably connected,” or “operably coupled,” to eachother to achieve the desired functionality.

References, incorporated herein by reference in their entirety for theirrespective relevant teachings:

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1. A method for measuring ocular distortion present in an eye,comprising: illuminating a known target pattern having characteristicfeatures and positioned in plane of an illumination path of a retinalimaging system that is conjugate to a retinal plane; projecting an imageof the target pattern onto an area of the retinal plane to provide adistorted retinal image of the target pattern across the area of theretinal surface; projecting the distorted retinal image of the targetpattern to an image sensor positioned in an imaging path of the retinalimaging system; recording the distorted retinal image of the targetpattern using the image sensor to provide a captured distorted retinalimage of the target pattern across the area of the retinal surface;identifying the characteristic features of the captured distortedretinal image of the target pattern across the area of the retinalsurface; and comparing the identified characteristic features of thecaptured distorted retinal image of the target pattern across the areaof the retinal surface to corresponding characteristic features of theknown target pattern to provide a map of ocular distortion across thearea of the retinal surface.
 2. The method of claim 1, comprisingquantifying the amount of ocular distortion and/or determining the typeof ocular distortion.
 3. The method of claim 1, wherein the retinalimaging system comprises a fundus camera configured to include the knowntarget pattern having the characteristic features and positioned inplane of the illumination path of the fundus camera that is conjugate toa retinal plane.
 4. The method of claim 1, wherein the target patterncomprises fiducials suitable for quantifying ocular distortion, thepattern being one or more selected from the amplitude pattern groupconsisting of a rectilinear grid pattern, concentric ring pattern, andvariable density grid pattern, or from the phase pattern groupconsisting of a phase plate, diffractive optic, and null correctingplate.
 5. The method of claim 1, wherein the target pattern is a phasetarget pattern placed in the illumination path of the retinal imagingsystem at a location that is a Fourier transform plane of the retina,the phase target having a suitable pattern such that its Fouriertransform creates a suitable amplitude pattern on the retina formeasuring ocular distortion.
 6. The method of claim 5, wherein theamplitude pattern comprises one or more of a grid pattern, concentriccircles, or arrays of dots.
 7. The method of claim 1, whereinidentifying the characteristic features of the captured distortedretinal image of the target pattern across the area of the retinalsurface, comprises use of frequency space image processing toautomatically detect centroid signatures from the target pattern.
 8. Themethod of claim 7, wherein a variable frequency pass method is sweptacross the image to remove intensity variations, and suppress strongobjects (e.g., optic disc, blood vessels) while boosting the distortedtarget pattern to the foreground.
 9. The method of claim 8, whereincentroid or corner detection is completed to identify various gridpositions across the retinal surface.
 10. The method of claim 2, whereinquantifying the amount of ocular distortion and/or determining the typeof ocular distortion comprises using mathematical fitting.
 11. Themethod of claim 10, comprising correlating, using a mathematical fittingalgorithm, the distorted retinal image of the target pattern across thearea of the retinal surface, with the known grid pattern, free fromdistortion, across the area of the retinal surface.
 12. The method ofclaim 11, comprising calibration of the retinal imaging system forinherent distortion to provide for an absolute measurement of oculardistortion.
 13. The method of claim 1, wherein the retinal imagingsystem is configured to distinguish between distortion caused by retinalimaging system misalignment and local changes in retinal topography, toprovide for extracting a diagnostic of global and local retinalcurvature.
 14. The method of claim 1, further comprising repetition ofthe method steps across one or more additional areas of the retinalsurface to provide a map of ocular distortion across a larger retinalarea, preferably comprising use of a stitching algorithm.
 15. The methodof claim 14, comprising combining eye rotation and continuous distortionmapping.
 16. The method of claim 1, further comprising use of suitablereal-time image processing software to examine features in the retinalimage (e.g., blood vessels) to provide autofocus of the image and toensure alignment of the eye between consecutive images.
 17. The methodof claim 16, wherein the retinal imaging system and software areconfigured to track the human eye and gaze direction ensuring eyealignment, to provide useful information on eye orientation duringdistortion measurement.
 18. The method of claim 1, applied to a subjectover time, to provide a method for monitoring or tracking temporalchanges or progression in distortion.
 19. The method of claim 18,applied during critical years of eye growth.
 20. The method of claim 18,comprising monitoring or tracking the change or progression indistortion relative to a distortion metric, to provide a method fordetecting early onset myopia or hyperopia in patients.
 21. The method ofclaim 20, further comprising implementing intervention methods (e.g.,corrective or control measures for ocular distortion such as spectaclelenses, contact lenses, multifocal lenses, and other modalities).
 22. Asystem for measuring ocular distortion present in an eye, comprising: aretinal imaging system comprising an illumination system having anillumination source and an illumination path, and an imaging systemhaving an image sensor and an imaging path, wherein the illuminationpath is configured to project light from the illumination source to aretinal plane of an eye to illuminate a retinal surface area, andwherein the imaging path is configured for projecting scattered lightemerging from the illuminated retinal surface area to the image sensor;and a known target pattern having characteristic features positioned andconfigured in a plane of the illumination path that is conjugate to theretinal plane, such that upon illumination an image of the known targetpattern is projectable onto the retinal surface area to provide for adistorted retinal image of the target pattern across the area of theretinal surface, and wherein the image sensor is configured forrecording of the distorted retinal image of the target pattern acrossthe area of the retinal surface.
 23. The system of claim 22, furthercomprising computer implemented software for identifying thecharacteristic features of the captured distorted retinal image of thetarget pattern across the area of the retinal surface, and comparing theidentified characteristic features of the captured distorted retinalimage of the target pattern across the area of the retinal surface tocorresponding characteristic features of the known target pattern toprovide a map of ocular distortion across the area of the retinalsurface.
 24. The system of claim 22, further comprising computerimplemented software for real-time image processing of features in theretinal image (e.g., blood vessels) to provide for autofocus of theimage and for eye alignment between consecutive images.
 25. The systemof claim 24, wherein the retinal imaging system and software areconfigured to track the eye and gaze direction to provide for eyealignment, to provide useful information on eye orientation duringdistortion measurement.
 26. The system of claim 22, wherein the targetpattern comprises fiducials suitable for quantifying ocular distortion,the pattern being one or more selected from the amplitude pattern groupconsisting of a rectilinear grid pattern, concentric ring pattern, andvariable density grid pattern, or from the phase pattern groupconsisting of a phase plate, diffractive optic, and null correctingplate.
 27. The system of claim 22, wherein the target pattern is a phasetarget pattern placed in the illumination path of the retinal imagingsystem at a location that is a Fourier transform plane of the retina,the phase target having a suitable pattern such that its Fouriertransform creates a suitable amplitude pattern on the retina formeasuring ocular distortion.
 28. The system of claim 26, wherein theamplitude pattern comprises one or more of a grid pattern, concentriccircles, or arrays of dots.
 29. The system of claim 22, wherein theretinal imaging system comprises a fundus camera.
 30. A method formeasuring retinal shape, comprising: displaying, to a subject, an imageof a known distorted target pattern having characteristic features andcharacterized in terms of size and shape relative to the distance awayfrom the subject's eye; tracking the edge points of the distorted targetpattern during correction or un-distortion of the distorted targetpattern by the subject, until the characteristic features areundistorted to provide a baseline measure for the amount of distortionpresent in the subject's eye; placing a lens of a given power in frontof the subject's eye; tracking the edge points of the distorted targetpattern during correction or un-distortion of the distorted targetpattern by the subject, until the characteristic features areundistorted to provide a measure for the amount of distortion present inthe subject's eye; and determining, using the baseline measure and thelens measure, a radius and/or shape (e.g., conic shape) of the subject'sretina (e.g., based on the presumption that the amount of distortionchanges equivalently for a given retinal shape).
 31. The method of claim30, wherein the distorted target pattern comprises a grid pattern havingdistorted lines, and wherein correction or un-distortion of thedistorted target pattern by the subject comprises correcting orun-distorting the distorted grid lines of the grid until they arestraight and square.
 32. The method of claim 30, wherein correction orun-distortion of the distorted target pattern by the subject andtracking thereof to determine residual distortion and characteristics ofretinal shape or asphericity from the corrected target comprises use ofsuitable software (e.g. image processing, mathematical fittingalgorithms, tracking, alignment, etc.).